AI Keyword List Filtering for Content Recommendation Accuracy
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Solution Overview
Problem
Digital content delivery systems face inefficiencies due to outdated negative keyword lists that incorrectly exclude relevant content, leading to increased processing power and network overhead.
Innovation Solution
An AI model is trained to generate recommendations for removing keywords from keyword lists based on a summary of the content provider's digital content and audience, integrating user feedback to improve accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If negative keyword lists are expanded over time to cover more exclusion cases, then content filtering accuracy improves, but processing power and network overhead increase
Solution Approach 1:
The patent extracts and removes outdated, irrelevant, or redundant keywords from the negative keyword list. By taking out only the necessary keywords that still serve their filtering purpose while eliminating the rest, the system maintains content filtering accuracy while significantly reducing the list size, thereby decreasing processing power requirements and network overhead for transmitting and comparing keywords.
2Reliability
If negative keyword lists are expanded over time to cover more exclusion cases, then content filtering accuracy improves, but network overhead increases
Solution Approach 1:
The system extracts and removes unnecessary keywords from the negative keyword list, reducing the overall list size. This extraction process eliminates redundant data that no longer serves the filtering purpose, thereby maintaining filtering accuracy while significantly reducing the network bandwidth required to transmit and synchronize keyword lists across the digital content delivery network.
3Measurement precision
If keyword lists are maintained manually over time, then keyword accuracy improves, but maintenance time and labor increase
Solution Approach 1:
The system implements self-service automation where the AI model automatically analyzes the digital content portfolio, identifies outdated or irrelevant keywords, and generates recommendations for removal. This self-service approach eliminates the need for manual keyword list maintenance while preserving keyword accuracy, as the AI continuously adapts to content changes and autonomously optimizes the negative keyword list without requiring human intervention or time investment.
Data Source
AI summary
Methods, systems, and apparatus, including computer-readable storage media, for keyword list filtering as part of identifying digital content responsive or relevant to a search query or request for content. A user, such as a content provider, may generate a keyword list associated with digital content of the content provider. Keyword lists, however, may be built over the course of years and can grow to include millions of keywords. Further, these keyword lists are often not maintained in line with changes in a content provider's digital content delivery strategy or context. An artificial intelligence (AI) model may be trained to generate a summary of the digital content associated with the content provider. That summary, along with the keyword list of the content provider, is provided as input into the AI model, which is trained to provide, as output, a recommendation to keep or remove a keyword from the keyword list.